Additive manufacturing (AM) offers significant advantages in industries such as machinery, automotive, and aerospace. However, challenges remain, such as surface roughness, oxidation, uneven melt pool temperature, and internal defects (e.g., porosity and cracks) during the metal material forming process. Machine vision technology can effectively improve manufacturing quality and stability through real-time detection and defect prediction. This paper systematically re-views the integration of machine vision with various imaging technologies in AM, focusing on the principles, core components, and algorithm optimization of visible light, infrared thermal, ultrasonic, X-ray, and spectral imaging. High-speed CMOS cameras enable real-time data acquisition, while deep learning algorithms significantly enhance defect detection accuracy and automation capability Additionally, multi-modal data fusion, such as infrared-visible joint monitoring, optimizes the system’s adaptability in complex environments. In the future, the combination of intelligent algorithms, such as generative adversarial networks and transfer learning, with lightweight equipment will further advance AM towards high precision, real-time feedback, and unmanned operations. This will provide key technical support for intelligent manufacturing.

This study proposes a novel radial synchronous loading (RSL) forming process for fabricating magnesium (Mg) alloy empennage-shaped components. The effects of axial loading (AL) and RSL processes on the metal flow behavior, microstructure evolution, and mechanical properties of the components were systematically investigated through Deform-3D finite element simulation (FEM) and forming experiments under the same process parameters. The results demonstrated that during RSL process, the core metal flowed uniformly toward the radial direction of the wings, forming radially ordered metal flow lines. Compared to AL, the components fabricated via RSL process exhibit higher equivalent strain values and superior strain uniformity. During the RSL process, the wing parts were subjected to the combined stress of radial extrusion and planar compression, which effectively promoted the dislocation movement, increased the dislocation density and the proportion of dynamically recrystallized (DRXed) grains, thereby effectively enhanced the mechanical properties of the empennage-shaped components. Compared with the AL process, the yield strength (YS) and ultimate tensile strength (UTS) of the RSL formed components along the ED direction were increased by 50.9% and 62.1%, respectively. The strength enhancement was mainly closely related to the DRXed grains, the dislocation density, and the orientation of the block-shaped LPSO phase, in which the short rod-like fiber strengthening of the block-shaped LPSO phase was the main strengthening mechanism.

PurposeThis study aims to address the inefficiency of conventional multidisciplinary design optimization (MDO) methods in handling the implicit relationship between structural parameters and performance responses, as well as the complex coupling relationships among multiple performance responses during intelligent large-scale equipment optimization.Design/methodology/approachAn improved collaborative optimization (CO) framework is developed through two key innovations. (1) An ensemble surrogate model combining radial basis function (RBF), Kriging and support vector regression (SVR) models is developed through linear weighting for enhanced approximation accuracy. (2) A modified CO algorithm incorporating dynamic penalty functions (ICO-DP) is proposed to effectively manage multiple coupling performance responses like mass minimization, fatigue life maximization and first-order modal frequency maximization. The methodology is validated using an earth pressure balance shield machine cutterhead as an engineering case study.FindingsThe optimization results show that the proposed ICO-DP method based on an ensemble surrogate model has obvious advantages over traditional methods, the mass is reduced by 3.2%, the first mode frequency is increased by 21.9% and the fatigue life is increased by 90%, which significantly improves the performance of the cutterhead.Originality/valueThis work makes three original contributions: (1) Ensemble surrogate model is applied to the design optimization of the cutterhead. (2) Integration of dynamic penalty functions with CO framework is conducted for coupled performance handling. (3) A complete methodological framework is built that bridges the gap between theoretical MDO research and practical large-scale equipment design applications.

The operational optimization of the coal mine integrated energy system (CMIES) is crucial for reducing costs and carbon emissions. However, the system's multi-objective nature, stringent constraints, and the uncertainty of renewable and mine-derived energy make solving its optimization challenging. Thus, this paper first presents a data-driven uncertainty transformation method to address the uncertainty of renewable energy and mining derived energy output; then, a multi-task multi-objective evolutionary algorithm based on adaptive auxiliary tasks (MMOEA-AS) is proposed, which includes a main task and three auxiliary tasks. Meanwhile, an adaptive update strategy for auxiliary tasks and a matching degree-guided knowledge transfer mechanism are proposed to improve the performance of the algorithm. Finally, taking the energy scheduling problem of a coal mine in Shanxi, China as an example, MMOEA-AS is compared with five advanced evolutionary algorithms. The results show that MMOEA-AS can effectively solve the operation optimization of the CMIES, and obtain the optimal scheduling results.

To achieve efficient reliability analysis and effective weight reduction of hinge sleeve, a reliability-based design optimization (RBDO) method based on the Bayesian optimization algorithm and adaptive ensemble of support vector machine with different kernel functions (E-SVM) is proposed in this paper. Firstly, the Bayesian optimization algorithm is used to search the optimal ensemble weights of the polynomial and Gaussian kernel functions in SVM. Based on this, the two kernel functions are linearly weighted, and the optimal hyperparameters of the SVM model are determined using the K-fold cross-validation to ensure the robustness of surrogate modeling. Secondly, an adaptive reliability analysis method for the E-SVM model is used, which quantifies the uncertainty of the empirical estimation of failure probability by calculating the bounded failure probability range. In each iteration, information parameter point is selected and added to the training sample set to achieve adaptive update of the SVM model. The effectiveness of the proposed method is verified by numerical examples and the application of the automobile front axle. Finally, the proposed method is applied to the lightweight design of hinge sleeve. Under the reliability requirements of stress, deformation, and lifespan, the weight of the hinge sleeve after optimization is reduced by 202.6 kg, achieving a weight reduction ratio of 3.3%.

To ease the choosing difficulty of RBF, Kriging and corresponding basis/correlation functions for unknown implicit problem in RBDO, a novel double ensemble (DE) modelling strategy was developed. Firstly, different basis/correlation functions of RBF and Kriging were weighted to obtain the ensemble RBF and ensemble Kriging models, which were further weighted to obtain the DE model. A heuristic weight calculation method was used to calculate weight coefficients of each sub surrogate and sub-ensemble model. To generate samples evenly and ensure the accuracy of the DE model around the initial design point, the inherited latinized centroidal Voronoi tessellation (ILCVT) sampling method was developed. Finally, the lightweight design of the cutterhead was performed by calling the DE model. Monte Carlo simulation and sequential quadratic programming were used to calculate the failure probability and iterative design point. After optimization, the mass of the cutterhead was reduced by 13.8

Purpose This study aims to improve the force sensing performance of the robot joint for the safety and flexibility of physical human–robot interaction. Design/methodology/approach A force sensing mechanism (FSM) for an S-shaped spring of a robot variable stiffness actuator (VSA) was designed. The yield strength of the spring material, geometric and assembly structure constraints of the VSA are all considered for the actuator deflection limit design. The elastic deformation model is solved in reverse to obtain the local deformation limit profile of the S-spring at different spring angles. The deformation limit mechanism is manufactured by three-dimensional printing and assembled with S-springs. The force sensing function for the VSA is achieved by the input and output shaft encoders and stiffness model. The FSM is verified by torque-deflection experiments with variable stiffness. Findings The yield strength of the S-spring material is the strictest constraint for elastic deformation. Experimental results show that the external force can be quickly and reliably perceived. As the spring angle increases (stiffness increases), the hysteresis and nonlinear error decrease. Under the constraint of the FSM, the maximum deflection also decreases rapidly. Originality/value The designed FSM based on the deformation and stiffness model provides a comprehensive design reference in a VSA with nonlinear elastic mechanisms, which is ignored but important for exploring the VSAs potential.

In this paper, a comprehensive overview was conducted on machine vision in potato cultivation, harvesting, and storage. Common weeds and diseases encountered during potato cultivation were summarized, and the advantages and disadvantages of various detection methods were compared. Additionally, methods for soil clod separation and tuber damage detection during harvesting were reviewed, along with a comparative analysis of their strengths and weaknesses. Furthermore, the defect grading and sprouting detection methods during storage were discussed. While machine vision technology shows good detection ability in potato cultivation, harvesting, and storage, further research is still needed to enhance the accuracy and adaptability of these methods, ultimately promoting the development of the potato industry.

Robot grinding requires a constant interaction force between the tool and the workpiece, even under inclination changes. This paper proposes a compact single-axis pneumatic constant-force floating compensator (CFFC) to achieve constant force output. The proportional pressure valve and pressure sensor are used to regulate the cylinder's pressure. Pneumatic components and sensors are integrated into the narrow space between the cylinder and the slide rail. Embedded controller, power, and communication modules are developed and integrated into a control box and interact with the operator by a touch screen. The mathematical models of the compensator are established and the stability and response dynamics are analyzed through transfer functions. A dual-loop force controller based on active disturbance rejection control (ADRC) is designed to address bias load, inclination change, friction, and the sealing cover spring effect. The outer loop is compensated by displacement, tilt, and pressure sensors, and the unmodeled dynamics are estimated by an extended state observer (ESO) and a recursive least square (RLS). Finally, the CFFC is installed on a testing platform to simulate grinding conditions. The experimental results show that even under large floating stroke, inclination changes, and biased load, the CFFC can still quickly and stably output the desired grinding force.

The axle bridge plays a crucial role in the bogie of low-floor light rail vehicles, impacting operational efficiency and fuel economy. To minimize the total cost of the structure and turning of axle bridges, an optimization model of structural and turning parameters was built, with the fatigue life, maximum stress, maximum deformation, and maximum main cutting force as constraints. Through orthogonal experiments and multivariate variance analysis, the key design variables which have a significant impact on optimization objectives and constraints (performance responses) were identified. Then the optimal Latin hypercube design and finite element simulation was used to build a Radial Basis Function (RBF) model to approximate the implicit relationship between design variables and performance responses. Finally, a multi-island genetic algorithm was applied to solve the integrated optimization model, resulting in an 8.457% and 1.1% reduction in total cost compared with the original parameters and parameters of sequential optimization, proving the effectiveness of the proposed method.

Metallic glasses (MGs), also known as amorphous alloys, has a unique atomic structure with long-range disorder and short-range order. MGs has excellent mechanical properties and is favored by many industries, such as, aerospace, medical devices, electronics and electricity, sports and leisure, etc. However, large-size MGs is hardly prepared in engineering due to the limited glass-forming ability (GFA). Moreover, the high hardness and low plasticity of MGs make the forming and machining difficult, which hindered its widespread application. Therefore, many researchers have focused on improving the preparation, forming, and machining ability of MGs. In this paper, the latest developments on preparation and machining are summarized, and a comprehensive review of MGs forming was firstly conducted. Then, the crystallization, MGs size range, surface roughness of different processes are discussed statistically. Finally, future research for the preparation, forming, and machining of MGs are discussed and prospected, and other research hotspots are analyzed. Furthermore, with the gradual maturity of MGs production technology, the importance of green manufacturing for sustainable development is emphasized, and several suggestions are put forward.

Abstract A PSO-GA-based method is proposed to improve the efficiency of complex product assembly sequence planning. We minimize total assembly time by optimizing the assembly sequence, considering the number of tool changes and assembly direction changes as evaluation criteria. A genetic algorithm (GA) is introduced into particle swarm optimization (PSO) to solve and verify assembly sequence planning. Finally, the effectiveness and rationality of the hybrid algorithm are verified by taking a certain type of gear pump as an example.

At present, there are three main methods for analyzing the causes of ship collision accidents: statistical analysis, accident causation models, and knowledge graphs. With the deepening of research, the analysis methods pay more attention to the objective correlation between various factors of the accident, and the analysis results obtained are more objective and accurate. On this basis, this paper proposes a method for analyzing the contribution degree of different causes and accident conduction paths in ship collision accidents based on the construction of the Ship Collision Accidents Event Graph (SCAEG). Firstly, the ontology is constructed based on the grounded theory. Secondly, events and relationships are extracted after fine-tuning the UIE model. Thirdly, the SCAEG is constructed after event coreference resolution. Finally, this research conducts the contribution degree analysis, accident conduction path analysis, and accident spatial distribution analysis based on SCAEG. The advantages of this method include the following: (i) it can construct a more complete and accurate ontology; (ii) adopting this approach can unify various information extraction tasks and achieve good results based on small sample annotation data; and (iii) using this method, we can conduct contribution degree analysis of different causes, accident conduction path analysis, and spatial distribution analysis. Experimental evidence demonstrates the effectiveness of this method. The analytical results obtained from the experiments can provide assistant decision-making for relevant departments to reduce the occurrence of ship collision accidents and improve maritime traffic safety.

Fe90 alloy has a high weld hardness, good toughness, and high oxidation resistance, and is often used as a cladding material to repair the surfaces of 42CrMo steel structures of large shearer picks. The influence of the laser cladding processing parameters on the microstructure, properties, and formation mechanism of Fe90 alloy layers on the surface of 42CrMo steel was studied. Simulations were conducted to investigate how these processing parameters affect the temperature field and internal stress of the cladding layer. A complex nonlinear relationship between variables and residual stresses in the laser cladding layers obtained by additive manufacturing was fitted. An optimization model for residual stress in the cladding layer was established and an improved genetic algorithm was used for optimization, which resulted in a 15.88% reduction in residual stress. The results show that optimizing the processing parameters increased the amount of Ni-Cr-Fe solid solution in the cladding layer, enhancing its strength and corrosion resistance. The amount of residual stresses rose with increases in laser power, but at higher powers, increasing the scanning speed and spot diameter reduced stresses. At lower powers, the amount of residual stresses initially increased and then decreased with the scanning speed, with more significant changes occurring with larger spot diameters. Analyzing temperature and residual stress changes allowed us to improve the cladding layer quality, providing a theoretical basis for laser cladding on 42CrMo.

Electronic waste, commonly known as "e-waste", refers to electrical or electronic equipment that has been discarded. E-waste, especially waste-printed circuit boards (WPCBs), must be handled carefully; as they can cause serious environmental pollution and threaten the health of local residents. The most abundant metal in WPCBs is copper, in addition to gold, aluminum, nickel, and lead, with grades that are tens or even hundreds of times higher than those of natural deposits. Due to the superiority of biorecovery methods in terms of their environmental friendliness, low capital investment and low operating costs, this study focuses on recent advances in the bioleaching and biosorption of metals from WPCBs. First, the principles, methods, and efficiency of bioleaching are reviewed in detail, particularly acidolysis, redoxolysis, and complexolysis. Additionally, six major factors (microbes, pH, temperature, nutrients, aeration, and substrate) affecting bioleaching are analyzed. The principles, kinetics, and isotherms of biosorption are then reviewed, and the factors influencing biosorption, including temperature and pH, are elaborated on. Hybrid recovery with biorecovery is explored, as these integrated strategies are conducive to achieving selective and efficient metal recovery. Finally, we discuss the advantages and disadvantages of the bioleaching and biosorption processes for metal recovery from WPCBs, particularly in terms of recovery efficiency, recovery time, and cost. Furthermore, future developments in biorecovery are also examined, along with useful ideas on how to accomplish energy-efficient metal recovery from WPCBs in the future.

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Ultrasonic-assisted wire–arc additive manufacturing (WAAM) can refine microstructures, enhancing performance and improving stress concentration and anisotropy. It has important application prospects in aerospace, weaponry, energy, transportation, and other frontier fields. However, the process parameters of ultrasonic treatment as an auxiliary technology in the WAAM process still have an important impact on product performance indicators, such as the amplitude of the ultrasonic tool, the distance between the points of action of the product, and the scanning speed. The number of ultrasonic impacts influences the performance indexes. Therefore, these parameters must be optimized. This paper describes the advantages and the defects of WAAM components, as well as the principle and development status of ultrasonic treatment technology. Subsequently, this paper also briefly describes how ultrasonic-assisted technology can refine the crystal and improve the mechanical properties of WAAM components. Finally, we review the influence of process parameters (such as ultrasonic amplitude, application direction, and impact times) on the product materials. In this paper, a comprehensive optimization method for ultrasonic parameters is proposed to improve the mechanical properties of WAAM components.

Thermal barrier coatings' (TBCs) stability and toughness are essential for insulating aero-engine parts. However, during service, the thickness of thermally grown oxide (TGO) will have an impact on the growth of internal vertical and horizontal cracks as well as the cracking and spalling of the coating, which may ultimately fail as a result of the aforementioned factors. The competitive growth of vertical and horizontal fractures in three functional gradient thermal barrier coatings (FG-TBCs) created with varied gradient index P magnitudes is investigated in this study. Gaining a thorough grasp of how competitive growth affects coating failure under various compositional and gradient structural parameters is the goal. Additionally, this work explores the effects of various TGO thicknesses on early crack expansion as well as the stress characteristics within the FG-TBCs. The research takes into account how crack growth changes the stress condition as well as the implications of crack extension rate, route, and form on coating failure. Our results show that the stress step phenomena in the FG-TBCs occurs in various places for various P values. The expansion of cracks in the top coat (TC) is fueled by this stress phase. Furthermore, a greater vertical crack extension length is obtained with a higher P value, with further expansion being prevented by horizontal cracks along the extension path. Notably, thicker TGO coatings change the direction and rate of expansion of cracks, both vertical and horizontal.